diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 137d3501e..0d23a605e 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1205,7 +1205,7 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
     const auto & use_mlock    = params.use_mlock;
     const auto & tensor_split = params.tensor_split;
 
-    const int n_layer = hparams.n_layer_all;
+    const int n_layer_all = hparams.n_layer_all;
     const int n_gpu_layers = this->n_gpu_layers();
 
     const bool use_mmap_buffer = true;
@@ -1262,10 +1262,10 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
         splits[i] /= split_sum;
     }
 
-    const int i_gpu_start = std::max(n_layer + 1 - n_gpu_layers, 0);
-    const int act_gpu_layers = devices.empty() ? 0 : std::min(n_gpu_layers, n_layer + 1);
+    const int i_gpu_start = std::max(n_layer_all + 1 - n_gpu_layers, 0);
+    const int act_gpu_layers = devices.empty() ? 0 : std::min(n_gpu_layers, n_layer_all + 1);
     auto get_layer_buft_list = [&](int il) -> llama_model::impl::layer_dev {
-        const bool is_swa = il < n_layer && hparams.is_swa(il);
+        const bool is_swa = il < n_layer_all && hparams.is_swa(il);
         if (il < i_gpu_start || (il - i_gpu_start) >= act_gpu_layers) {
             LLAMA_LOG_DEBUG("load_tensors: layer %3d assigned to device %s, is_swa = %d\n", il, ggml_backend_dev_name(cpu_dev), is_swa);
             return {cpu_dev, &pimpl->cpu_buft_list};
@@ -1281,13 +1281,13 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
     pimpl->dev_input = { cpu_dev, &pimpl->cpu_buft_list };
 
     // assign the repeating layers to the devices according to the splits
-    pimpl->dev_layer.resize(n_layer);
-    for (int il = 0; il < n_layer; ++il) {
+    pimpl->dev_layer.resize(n_layer_all);
+    for (int il = 0; il < n_layer_all; ++il) {
         pimpl->dev_layer[il] = get_layer_buft_list(il);
     }
 
     // assign the output layer
-    pimpl->dev_output = get_layer_buft_list(n_layer);
+    pimpl->dev_output = get_layer_buft_list(n_layer_all);
 
     const auto TENSOR_NOT_REQUIRED = llama_model_loader::TENSOR_NOT_REQUIRED;
 
@@ -1303,14 +1303,14 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
             throw std::runtime_error("model has expert layers but no expert layers are used");
         }
 
-        layers.resize(n_layer);
+        layers.resize(n_layer_all);
 
         // call the per-model loading function
         load_arch_tensors(ml);
 
         // generic pass: load optional per-tensor/per-expert ".scale" tensors (e.g. NVFP4 scale2)
         // this avoids having to add scale loading to every architecture
-        for (int i = 0; i < n_layer; ++i) {
+        for (int i = 0; i < n_layer_all; ++i) {
             auto & layer = layers[i];
 
             // attention weight scales (per-tensor, shape {1})
@@ -1568,7 +1568,7 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
     }
 
     if (llama_supports_gpu_offload()) {
-        const int n_gpu = std::min(n_gpu_layers, n_layer);
+        const int n_gpu = std::min(n_gpu_layers, n_layer_all);
 
         int n_repeating = n_gpu;
         if (n_repeating > 0) {
@@ -1577,8 +1577,8 @@ bool llama_model_base::load_tensors(llama_model_loader & ml) {
         }
         LLAMA_LOG_INFO("%s: offloading %d repeating layers to GPU\n", __func__, n_repeating);
 
-        const int max_backend_supported_layers = n_layer + 1;
-        const int max_offloadable_layers       = n_layer + 1;
+        const int max_backend_supported_layers = n_layer_all + 1;
+        const int max_offloadable_layers       = n_layer_all + 1;
 
         LLAMA_LOG_INFO("%s: offloaded %d/%d layers to GPU\n", __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers);
     }